Clustering documents by their content before applying mention-level event coreference boosts performance on the ecbp corpus [1]}, [2]}, [3]}, [4]}. As was recommended by [1]}, we report results for several document preclustering strategies to better distinguish the source of performance gains or losses.
We compare (1) no preclustering, (2) the gold document clusters, and (3) the k-means clustering approach used in [2]}.
The gold document clusters are defined via the transitive closure of all event coreference links.
For gvc, this gold document clustering is identical to the corpus subtopics. For the fcct test split, the gold clustering is a single cluster containing all documents which is equivalent to not applying document clustering at all.
For ecbp, the gold clustering largely corresponds to the corpus subtopics with the exception of some subtopics which are merged due to cross-subtopic event coreference links.
In the k-means approach, all \(n\)  input documents are represented by TF-IDF vectors based on which all possible k-means clusterings for \(k=2,\ldots , n\)  are created.
From these clusterings, the one with the highest silhouette score [7]} is used.
